Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Reward pursuit during a translational reward task correlates with anhedonia reductions following rTMS in patients with major depressive disorder.

Translational psychiatry·2026
Same author

Prefrontal cortical pathways mediating cognitive control enhancement from internal capsule stimulation.

bioRxiv : the preprint server for biology·2026
Same author

Unilateral striatal deep brain stimulation improves cognitive control.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Sex-biased computations underlying differential set shift performance in mice.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Female rats adopt a safety-first strategy in a high-conflict platform mediated avoidance task.

Frontiers in behavioral neuroscience·2026
Same author

Author Correction: Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation.

Nature biomedical engineering·2026

Related Experiment Video

Updated: Jul 5, 2026

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
11:12

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Published on: July 16, 2014

23.0K

Real-time Bayesian optimization of deep brain stimulation for personalized cognitive control enhancement.

Evan M Dastin-van Rijn1, Elizabeth M Sachse2, Michelle Buccini3

  • 1Department of Biomedical Engineering, University of Minnesota, Nils Hasselmo Hall, 7-105, 312 Church St. SE, Minneapolis, MN 55455.

Biorxiv : the Preprint Server for Biology
|January 9, 2026
PubMed
Summary

Optimization algorithms can effectively identify deep brain stimulation (DBS) parameters to improve cognitive control in rats. This study demonstrates a faster, personalized approach to neuromodulation for psychiatric and cognitive disorders.

Keywords:
Bayesian optimizationCognitive controlNeuromodulationPsychiatrySet-shiftdeep brain stimulationinternal capsule

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.8K

Related Experiment Videos

Last Updated: Jul 5, 2026

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
11:12

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Published on: July 16, 2014

23.0K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.8K

Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomarkers

Background:

  • Deep brain stimulation (DBS) parameter optimization for psychiatric disorders is challenging due to the lack of objective readouts for target engagement.
  • Cognitive control shows potential as a biomarker for DBS treatment efficacy, but reliable optimization for individual patients remains unproven.

Purpose of the Study:

  • To investigate the efficacy of optimization algorithms in identifying effective DBS amplitudes for enhancing cognitive control in a rat model.
  • To determine if state-of-the-art optimization can consistently improve cognition through precise stimulation parameter selection.

Main Methods:

  • Rats performed a Set-Shifting task, a stimulation-sensitive cognitive control measure.
  • Active and inactive DBS-like stimulation were delivered at variable parameters.
  • Bayesian Optimization was used to personalize stimulation amplitudes, comparing task performance (reaction time, accuracy) to predefined and traditional settings.

Main Results:

  • Acute DBS stimulation reduced reaction times without affecting accuracy in 15 rats, confirming previous findings.
  • In a separate cohort of 6 rats, Bayesian Optimization successfully identified stimulation amplitudes that reduced reaction times in all subjects.

Conclusions:

  • Optimization techniques, particularly Bayesian Optimization, can effectively enhance cognitive markers relevant to psychiatric and cognitive disorders.
  • These findings support the feasibility of personalized, quantitatively-driven neuromodulation for improved target engagement and treatment efficacy.